Head of Data

Agriodor
is an innovative, impact-driven, and multi-award-winning SME founded in 2019, based on research into the relationship between crop pest insects and plants.

After an initial phase of laboratory and field development, the company patented its first solutions based on
Kairomones
and
Allomones
, which significantly reduce the use of phytosanitary products in crops — a major benefit for biodiversity. Agriodor is the
first company worldwide
to design this type of solution.

This rapid progress enabled the company to
raise funds
and earn the
French Tech 2030 label
.

With
two distribution contracts already signed
, Agriodor is now experiencing strong project acceleration. At the heart of this growth is the
expansion of target insects and crops
, including beet, potato, fruit trees (stone fruits, apple trees), berries, tropical fruits, and soon maize, soybean, and cotton.

Post description :

Context & Position Overview

The
Head of Data (Data management, data science, AI)
will lead the development and management of Agriodor's proprietary and external data, and the data strategy.

The Head of data will design and implement this strategy w
ith the support
and
to the service
of each domain :
R&D teams
(chemistry, entomology, agronomy, formulation) and other teams (marketing…)

The objective of the Head of Data, through its missions, are to :

• Ensure data-driven decision-making across the company
• Develop efficiency and robustness in data analysis within the company
• Prepare Agriodor's data architecture for future AI/ML applications
• Deploy of AI / ML solutions (AI agents, predictive models...) which are relevant to each domain

To reach those objective, the Data scientist :

• Will report directly to the Chief Technology Officer (CTO)
• Will structure the Data Department which will probably include recruitment needs and service provider to ensure that the above mentioned objectives are reached (Data Management, Biostat...)
• Will receive the support of the external CIO
• Will work closely with
  each domain

Key Responsibilities :

Database Management & Evolution

Develop, maintain, and optimize Agriodor's database. This responsibility includes :

• Deploy Data architecture management good practices with appropriate processes to ensure data quality, integrity, security, completeness and compliance.
• Set protocols to cleanse historical data
• Ensure proper collection of all Agriodor's relevant data with the help of each
  team
  and the relevant tools for data collection and data cleansing
• Train teams to fully utilize tools deployed and
  empower them
  so that they can guarantee quality of uploaded data
• Integrate external datasets (agronomic, environmental, entomological, literature, climate data):
• Design scalable data structures to future AI deployment (machine learning models…)

Data science & statistics

• Give access to each team relevant data and train them so that they are as autonomous as possible to perform the analysis they need for their work
• Develop new data analysis methods (data science or biostat)
• Collaborate with teams to ensure statistical robustness in experimental design.
• Support internal reporting, publications, and regulatory dossiers
• Train and support internal teams to strengthen statistical literacy across the company.

AI

• Contribute with the Executive Committee to long-term digital strategy, including predictive modeling with AI/ML, in silico screening...
• Conceive PoCs, Minimal Viable Products
• Pilot relevant LLM and AI tools access to R&D Teams and other functions (Marketing, Back …)
• Support teams in the creation of their own agents…

Profile required :

Profile & Requirements

• 10 years+ of experience in data management, data sciences and statistical analysis.
• Entry level experience in AI/Agents
• Experience in life sciences appreciated
• Master's or PhD in Data Science, Applied Mathematics, Computational Biology, Bioinformatics, Biostatistics, or related field.
• Expertise in experimental design, advanced statistical modeling, and analysis.
• Experience with databases (SQL / No SQL), R, Python, SAS, visualization tools.
• Familiarity with ML frameworks (scikit-learn, TensorFlow, PyTorch) is a plus.
• Fluent in French and English.
• Strong analytical mindset, rigor, autonomy,
  teamwork, and communication skills
  .

Required languages

• English - Level advanced
• French - Level advanced